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Comparing different clustering algorithms on to...
"connectivity matrix is [0-9]{1,2}" " > 1. Completing it to avoid stopping...= n_samples , cluster_std = [ 1.0 , 2.5 , 0.5 ], random_state...scikit-learn.org/stable/auto_examples/cluster/plot_cluster_comparison.html -
trustworthiness — scikit-learn 1.7.0 documentation
1]. It is defined as \[T(k) = 1 - \frac{2}{nk (2n...(2n - 3k - 1)} \sum^n_{i=1} \sum_{j \in \mathcal{N}_{i}^{k}}...scikit-learn.org/stable/modules/generated/sklearn.manifold.trustworthiness.html -
PoissonRegressor — scikit-learn 1.7.0 documenta...
predict ([[ 1 , 1 ], [ 3 , 4 ]]) array([10.676,...PoissonRegressor ( * , alpha = 1.0 , fit_intercept = True , solver...scikit-learn.org/stable/modules/generated/sklearn.linear_model.PoissonRegressor.html -
DummyClassifier — scikit-learn 1.7.0 documentation
([ - 1 , 1 , 1 , 1 ]) >>> y = np . array ([ 0 , 1 , 1 , 1 ]) >>>...dummy_clf . predict ( X ) array([1, 1, 1, 1]) >>> dummy_clf . score (...scikit-learn.org/stable/modules/generated/sklearn.dummy.DummyClassifier.html -
Face completion with a multi-output estimators ...
shape [ 1 ] # Upper half of the faces X_train...X_train = train [:, : ( n_pixels + 1 ) // 2 ] # Lower half of the faces...scikit-learn.org/stable/auto_examples/miscellaneous/plot_multioutput_face_completion.html -
PLSRegression — scikit-learn 1.7.0 documentation
1 , - 0.2 ], [ 0.9 , 1.1 ], [ 6.2 , 5.9 ],...intercept_ . Added in version 1.1. n_iter_ list of shape (n_components,)...scikit-learn.org/stable/modules/generated/sklearn.cross_decomposition.PLSRegression.html -
VarianceThreshold — scikit-learn 1.7.0 document...
1 , 4 , 3 ], [ 0 , 1 , 1 , 3 ]] >>> selector...fit_transform ( X ) array([[2, 0], [1, 4], [1, 1]]) fit ( X , y = None )...scikit-learn.org/stable/modules/generated/sklearn.feature_selection.VarianceThreshold.html -
Faces recognition example using eigenfaces and ...
1 )[ - 1 ] true_name = target_names...y_test [ i ]] . rsplit ( " " , 1 )[ - 1 ] return "predicted: %s \n...scikit-learn.org/stable/auto_examples/applications/plot_face_recognition.html -
SVM: Separating hyperplane for unbalanced class...
clusters of random points n_samples_1 = 1000 n_samples_2 = 100 centers...2.0 , 2.0 ]] clusters_std = [ 1.5 , 0.5 ] X , y = make_blobs (...scikit-learn.org/stable/auto_examples/svm/plot_separating_hyperplane_unbalanced.html -
DBSCAN — scikit-learn 1.7.0 documentation
1, 1, -1]) >>> clustering DBSCAN(eps=3,...means 1 unless in a joblib.parallel_backend context. -1 means...scikit-learn.org/stable/modules/generated/sklearn.cluster.DBSCAN.html